class_docstr: Benchmarks both quantization and dequantization.
code: def init(self, C, M, N, dtype, mode):
      assert mode in ("Q", "D")
      self.input = torch.rand(C, M, N)
      self.dtype = dtype
      self.op = nnq.Quantize(scale=1.0, zero_point=0, dtype=dtype)
      self.set_module_name("QuantizePerTensor")
  
      if mode == "D":
          self.input = self.op(self.input)
          self.op = nnq.DeQuantize()
          self.set_module_name("DequantizePerTensor")
  
      self.inputs = {"input": self.input}
  
imports: import torch
  import torch.ao.nn.quantized
  import torch.ao.quantization
  import torch.nn
  import operator_benchmark
label_desc: Use image node_id 1 for calling the QuantizePerTensorBenchmark obj's underlying member method code with example usage: obj.init(C, M, N, dtype, mode) without return types